Initial construction of a maladaptive personality trait model ...

Psychological Medicine (2012), 42, 1879?1890. Published by Cambridge University Press 2011 doi:10.1017/S0033291711002674 f 2011 American Psychiatric Association

ORIGINAL ARTICLE

Initial construction of a maladaptive personality trait

model and inventory for DSM-5

R. F. Krueger1*, J. Derringer1, K. E. Markon2, D. Watson3 and A. E. Skodol4

1 Department of Psychology, University of Minnesota, Minneapolis, MN, USA 2 Department of Psychology, University of Iowa, Iowa City, IA, USA 3 Department of Psychology, University of Notre Dame, Notre Dame, IN, USA 4 Department of Psychiatry, University of Arizona School of Medicine, Phoenix, AZ, USA

Background. DSM-IV-TR suggests that clinicians should assess clinically relevant personality traits that do not necessarily constitute a formal personality disorder (PD), and should note these traits on Axis II, but DSM-IV-TR does not provide a trait model to guide the clinician. Our goal was to provide a provisional trait model and a preliminary corresponding assessment instrument, in our roles as members of the DSM-5 Personality and Personality Disorders Workgroup and workgroup advisors.

Method. An initial list of specific traits and domains (broader groups of traits) was derived from DSM-5 literature reviews and workgroup deliberations, with a focus on capturing maladaptive personality characteristics deemed clinically salient, including those related to the criteria for DSM-IV-TR PDs. The model and instrument were then developed iteratively using data from community samples of treatment-seeking participants. The analytic approach relied on tools of modern psychometrics (e.g. item response theory models).

Results. A total of 25 reliably measured core elements of personality description emerged that, together, delineate five broad domains of maladaptive personality variation : negative affect, detachment, antagonism, disinhibition, and psychoticism.

Conclusions. We developed a maladaptive personality trait model and corresponding instrument as a step on the path toward helping users of DSM-5 assess traits that may or may not constitute a formal PD. The inventory we developed is reprinted in its entirety in the Supplementary online material, with the goal of encouraging additional refinement and development by other investigators prior to the finalization of DSM-5. Continuing discussion should focus on various options for integrating personality traits into DSM-5.

Received 13 April 2011 ; Revised 27 July 2011 ; Accepted 25 October 2011 ; First published online 8 December 2011

Key words : Assessment, DSM, personality disorders, personality traits.

Introduction

In DSM-IV-TR (Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision), if maladaptive personality traits are part of the clinical picture but do not constitute a formal personality disorder (PD), the clinician is encouraged to record these traits on Axis II. Nevertheless, DSM-IV-TR does not provide a specific model for conceptualizing these traits, beyond their appearance as features of the 10 PDs. This is in spite of the fact that there has been much interest recently in the etiology (van den Oord et al. 2008 ; de Moor et al. 2010), treatment (Tang et al. 2009), clinical relevance (Hopwood et al. 2008) and

* Address for correspondence : R. F. Krueger, Ph.D., Department of Psychology, University of Minnesota, N414 Elliott Hall, Minneapolis, MN 55455, USA.

(Email : krueg038@umn.edu)

social costs (Cuijpers et al. 2010) of personality traits in the psychiatric literature.

Our aim in the current research was (a) to construct a preliminary maladaptive personality trait model for DSM-5 and then (b) to test and refine it through the creation of a provisional corresponding assessment instrument. This research was conducted under the auspices of the DSM-5 Personality and Personality Disorders Workgroup, by members of the workgroup and workgroup consultants. Nevertheless, it is critical to emphasize that no decisions have been formalized regarding the conceptualization of PDs in DSM-5, or regarding the ways in which the constructs of personality traits and PDs might best be represented in DSM-5. By disseminating our findings to date in this report, our hope is to stimulate additional research from other investigators that can inform DSM-5 and subsequent revisions.

1880 R. F. Krueger et al.

With regard to a personality trait model suitable for DSM-5, a variety of compelling models exist, instantiated in a corresponding variety of assessment instruments (for reviews, see Trull & Durrett, 2005 ; Clark, 2007). The DSM-5 workgroup and consultants (a number of whom are authors of published measures of clinically relevant personality constructs ; Morey, 2003 ; Livesley & Jackson, 2009 ; Clark et al., in press) began by reviewing these existing models and measures of maladaptive personality traits, with a particular focus on reviews completed as part of a 2004 pre-DSM-5 research planning meeting. In particular, Widiger & Simonsen (2005) provided evidence that four broad bipolar domains (i.e. domains with two opposite ends) of extraversion v. introversion, antagonism v. compliance, constraint v. impulsivity, and negative affect v. emotional stability could serve as an organizing framework for traits seen across 18 models that had been described in the literature. They also described a fifth potential domain, ` unconventionality v. closedness to experience ', but noted that this domain was not well represented in the models they reviewed, although it is a major domain assessed by the Revised NEO Personality Inventory (Costa & McCrae, 1992). Moreover, a recent meta-analysis showed essentially zero correlation between this domain and DSM-IV PDs (Samuel & Widiger, 2008). Nevertheless, other research has identified a domain of peculiar or odd traits that provides coverage of features corresponding with some key components of schizotypal PD, i.e. ` cognitive or perceptual distortions and eccentricities of behavior ' (DSM-IV-TR, APA, 1994 ; Harkness et al. 1995 ; Chmielewski & Watson, 2008). Hence, in addition to the four major domains identified by Widiger & Simonsen (2005), we also sought to identify and measure traits in a fifth domain of psychoticism, resulting in a model that, at the domain level, bears a strong resemblance to Harkness's Personality Psychopathology 5 model of clinically relevant personality variants (Harkness et al. 1995).

Our focus was initially on identifying and operationalizing specific maladaptive personality dimensions falling within five broad domains, with a focus on the poles of these domains that are associated with PD (i.e. introversion, antagonism, impulsivity, negative affect, and psychoticism). That is, the features of PD tend to be concentrated at specific poles of these domains. In a meta-analytic review of literature linking the Five Factor Model of personality (FFM ; see Goldberg, 1993 ; Costa & Widiger, 2002) ? which bears a strong resemblance to the model described by Widiger & Simonsen (2005) ? with the DSM-IV PDs (Samuel & Widiger, 2008), DSM-IV PDs were associated with introversion (the absence of FFM extraversion), antagonism (the absence of FFM

agreeableness), impulsivity (the absence of FFM conscientiousness) and negative affect (FFM neuroticism). There were only two exceptions : an association between histrionic PD and FFM extraversion, and an association between obsessive-compulsive PD and FFM conscientiousness. Hence, we endeavored to ensure that our trait list also covered core features of histrionic PD and obsessive-compulsive PD. DSM-IV describes the core features of histrionic PD as ` excessive emotionality and attention seeking ' (APA, 1994), so we ensured coverage of those two primary traits. DSM-IV describes the core features of obsessivecompulsive PD as ` preoccupation with orderliness, perfectionism, and control ' (APA, 1994). These traits broadly define the constraint pole of impulsivity v. constraint in the Widiger & Simonsen (2005) model. Hence, we focused on the delineation and measurement of specific maladaptive traits in the domains of (I, high pole) introversion, (II, high pole) antagonism, (III, high pole) impulsivity v. (III, low pole) constraint, (IV, high pole) negative affect, and (V, high pole) psychoticism. As described below, we subsequently changed the name of the introversion domain to detachment and the name of the impulsivity domain to disinhibition, to better reflect the content of these domains, at least as that content emerged in our project.

In sum, our approach was to synthesize existing models to arrive at a model and assessment instrument that (a) encompass the four major domains of maladaptive personality variation identified by Widiger & Simonsen (2005), with explicit measurement of the poles of those domains associated with DSM-IV-TR PDs ; (b) also contains an additional fifth domain of psychoticism ; and (c) contains multiple specific maladaptive trait facets within all five domains, with a focus on covering the maladaptive trait features of DSM-IV-TR PDs. To our knowledge, no existing model and assessment instrument encompasses this complete set of features.

An additional consideration regarding existing models is that these models are not suited to being imported verbatim into the DSM because they are typically operationalized in specific, commercially available assessment instruments. In sum, our approach was to draw broadly on research on existing models, to frame the generation of an empirically based model and measure that is freely available and can be employed in research that can inform DSM-5 and beyond.

Measure construction

We began with a hypothesized set of domains identified throughout the DSM-5 process to date as covering maladaptive personality variation in existing instruments and models. Work group members

Initial personality trait model and inventory for DSM-5 1881 Table 1. Original 37 facets, mapped to final 25 facets and five domainsa

Original facet (prior to Round 1 data collection)

Anxiousness Emotional lability Hostility Oppositionality Perseveration (lack of) Restricted Affectivity Separation Insecurity Submissiveness

Anhedonia Depressivity Guilt and Shame Low Self-Esteem Pessimism Self-Harm Intimacy Avoidance Suspiciousness Social Detachment Social Withdrawal

Attention Seeking Aggression Callousness Deceitfulness Grandiosity Manipulativeness

Distractibility Impulsivity Irresponsibility (lack of) Orderliness (lack of) Perfectionism (lack of) Rigidity Recklessness (lack of) Risk Aversion

Cognitive Dysregulation Eccentricity

Dissociation Proneness

Unusual Beliefs Unusual Perceptions

Restructured facet (after Round 2 analyses)

Anxiousness Emotional Lability Hostility Perseveration (lack of) Restricted Affectivity Separation Insecurity Submissiveness

Anhedonia

Depressivity

Intimacy Avoidance Suspiciousness Withdrawal

Attention Seeking Callousness Deceitfulness Grandiosity Manipulativeness

Distractibility Impulsivity Irresponsibility

(lack of) Rigid Perfectionism

Risk Taking

Eccentricity Cognitive and Perceptual Dysregulation Unusual Beliefs and Experiences

Final domain Negative Affect Detachment

Antagonism Disinhibition Psychoticism

a Rigid perfectionism and Restricted affectivity are indicators of the opposite pole of their respective domains (see Table 3).

and consultants generated a list of 37 facets (specific personality traits, as opposed to broad domains containing multiple traits) as potential exemplars with the aim of covering all the domains. Each of the 11 members of the DSM-5 Personality and Personality Disorders Workgroup had the opportunity to contribute to this process and were given multiple

opportunities to review the resulting list of facets. Once the preliminary list of 37 facets was finalized, the authors of the current paper wrote brief definitions of each of the facets, and relied on these definitions in writing items designed to tap their content.

Table 1 presents a list of these domains and facets, using the terminology we arrived at ultimately.

1882 R. F. Krueger et al.

Our initial measure construction took place over the course of two rounds of data collection, aimed at (a) measuring each proposed facet reliably, and (b) examining whether facets could be collapsed, or items reassigned among facets, within each of the domains. Both of these measure construction goals were completed by collecting data from an on-line panel of respondents specifically cultivated to ensure the ability to generalize to the US population (the Knowledge Networks Panel ; Dennis, 2010). Samples were selected from among persons in the Knowledge Networks Panel who responded positively to the question ` have you ever seen a therapist for psychological or psychiatric counseling or therapy ' in a previous survey. This sampling strategy was used in our initial rounds of data collection to help ensure that our respondents were more nationally representative than those who could be recruited from a specific clinic, but also more likely to show variation in maladaptive personality characteristics, relative to a sample not selected for seeking mental health services.

Round 1

The first round of data collection was designed to examine our ability to measure the 37 initial facets. We began by writing eight specific personality items to measure each proposed facet. The reading level of all items was eighth grade or less, to ensure that the resulting inventory could be completed by persons with varying levels of education.

Participants

A total of 762 persons participated in our initial round of data collection. Knowledge Networks collected all data via a web-based survey. Demographic characteristics for all three waves of data collection are presented in Supplementary Table S1. Sampling weights were applied in all analyses (Asparouhov, 2005) to adjust the current sample demographics to be representative of the US population (and, at Rounds 1 and 2, taking into account our study-specific inclusion criterion of having seen a psychiatrist or psychologist). The weighting took into account both sampling (e.g. under-sampling of telephone numbers not matched to a valid mailing address) and participant characteristics (i.e. sex, age, race/ethnicity, education, geographic location, living in a metropolitan area, and Internet access).

Measure

The Round 1 item pool consisted of 296 items (eight per each of 37 facets). Item order was randomized, and the items were split into four sections of 74 items each.

These sections were then combined into six booklets. That is, if the 74-item sections are designated ABCD, the six booklets were : AB, AC, AD, BC, BD, and CD. Participants were then randomly assigned to receive one of the six booklets, such that each participant received 50 % of the total measure and participant missingness across items was completely at random (see, e.g. Smits and Vorst, 2007). This reduced the response burden on each participant, while still ensuring that covariances were estimable among all items. Each item was displayed on a separate screen. All items presented the same four response options of ` Very false or often false ', ` Sometimes or somewhat false ', ` Sometimes or somewhat true ' and ` Very true or often true '. The entire final measure, including respondent instructions, item content, and response options, is given in Appendix A of the Supplementary material.

Analyses

Analyses were completed in the software package Mplus (Muthe?n & Muthe?n, 1998?2010) on raw data, weighted according to Knowledge Networks' determined sampling weights to adjust the data to be population representative, using a robust maximumlikelihood estimator to estimate model parameters and to model missingness directly (MLR), and treating categorical manifest variables (e.g. item-level responses) as categorical. Initially, Geomin-rotated exploratory factor analyses (EFAs) were run on all eight items within each facet, requesting factor results for one to four factors and treating the items as ordinal. Factor solutions were compared on their Bayesian information criterion (BIC ; Schwarz, 1978), with a lower BIC indicating a better relative fit to the data. Because we were fitting directly to the raw response patterns, fit indices that can be obtained for maximum likelihood (ML) estimation applied to moment matrices (e.g. covariances) were not available in this scenario. We relied on BIC because of its emphasis on parsimony, which corresponded with our aim of identifying parcels of items that measured a single factor (i.e. the most parsimonious model among models containing one to k factors). Specifically, if BIC indicated that a one-factor solution fit the data best, all items were retained for subsequent analyses. If BIC indicated that the best-fitting solution included more than one factor, we selected items loading on the largest factor (in terms of the greatest number of items) and re-ran the EFA to confirm that a one-factor solution fit best in the retained items.

We then fit one-factor models to items within each of the facets, with the aim of further refining these initial facet measures. Specifically, if any items had

Initial personality trait model and inventory for DSM-5 1883

Table 2. Means and standard deviations for facet and domain scores (calculated as the average response across items) and classical test theory reliability estimates (Cronbach's a)a

Selected sample, Round 2

Representative sample, Round 3

Items

a

Mean

(S.D.)

a

Mean

(S.D.)

Facet

Anhedonia

8

Anxiousness

9

Attention seeking

8

Callousness

14

Deceitfulness

10

Depressivity

14

Distractibility

9

Eccentricity

13

Emotional lability

7

Grandiosity

6

Hostility

10

Impulsivity

6

Intimacy avoidance

6

Irresponsibility

7

Manipulativeness

5

Perceptual dysregulation

12

Perseveration

9

Restricted affectivity

7

Rigid perfectionism

10

Risk taking

14

Separation insecurity

7

Submissiveness

4

Suspiciousness

7

Unusual beliefs and experiences

8

Withdrawal

10

Domain

Negative affect

53

Detachment

45

Antagonism

43

Disinhibition

46

Psychoticism

33

0.88

1.05

(0.65)

0.88

0.89

0.91

1.25

(0.78)

0.91

1.02

0.88

0.86

(0.63)

0.89

0.81

0.88

0.46

(0.44)

0.91

0.40

0.89

0.56

(0.54)

0.85

0.52

0.94

0.66

(0.65)

0.95

0.53

0.92

0.99

(0.72)

0.91

0.82

0.95

0.98

(0.77)

0.96

0.82

0.90

1.06

(0.74)

0.89

0.94

0.73

0.84

(0.55)

0.72

0.82

0.88

1.05

(0.63)

0.89

0.91

0.85

0.85

(0.67)

0.77

0.77

0.84

0.79

(0.69)

0.84

0.61

0.80

0.46

(0.50)

0.81

0.39

0.80

0.81

(0.61)

0.81

0.80

0.89

0.54

(0.55)

0.86

0.44

0.86

0.88

(0.61)

0.88

0.82

0.75

0.95

(0.56)

0.73

0.97

0.89

1.08

(0.65)

0.90

1.05

0.88

1.09

(0.53)

0.85

1.05

0.86

0.80

(0.68)

0.85

0.80

0.80

1.20

(0.68)

0.78

1.17

0.83

1.04

(0.67)

0.73

0.95

0.85

0.66

(0.62)

0.83

0.64

0.93

1.12

(0.71)

0.93

1.01

0.94

1.16

(0.44)

0.93

1.07

0.96

0.91

(0.54)

0.96

0.78

0.94

0.65

(0.42)

0.95

0.61

0.89

1.13

(0.35)

0.84

1.06

0.95

0.74

(0.56)

0.96

0.64

(0.64) (0.73) (0.65) (0.50) (0.54) (0.62) (0.69) (0.76) (0.74) (0.58) (0.67) (0.57) (0.65) (0.49) (0.67) (0.48) (0.62) (0.56) (0.68) (0.51) (0.68) (0.66) (0.58) (0.63) (0.72)

(0.44) (0.54) (0.46) (0.30) (0.57)

a Item responses were coded from 0 to 3, with higher scores indicating greater pathology. Statistics are based on list-wise deletion ; therefore, individuals from Round 1 are not included due to planned missingness. Items from Restricted affectivity and Rigid perfectionism were reverse-scored for their inclusion in the Negative affect and Disinhibition domain scores, respectively.

standardized loadings on their facet of less than 0.5, those items were dropped and the factor analyses were re-run with the retained items. We chose 0.5 as the minimum loading for retaining an item because this is a relatively conservative value, corresponding with high correlations among all items within a facet scale ; we found that a cut-off of 0.5 ensured our ability to create scales with relatively good information curves and high levels of internal consistency (see Supplementary material and Table 2 for evidence that this strategy was ultimately effective).

Once each facet had been reduced to items that (1) fit a one-factor model, and (2) all loaded highly on the

facet, we examined the results in an item response theory (IRT) framework (for a thorough description of IRT-based theory and methods, see Embretson & Reise, 2000). Using a two-parameter logistic IRT model (the two parameters are threshold and slope, with three thresholds per item in our case because the items had four response options) allowed us to estimate reliability for individuals contingent upon their latent trait level (h). We estimated information for both individual items and the overall facet (or test information, the sum of information curves for each of the included items). Facets were considered well measured if their test information was greater than 5

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